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Longitudinal cohort study of injury type, settings, treatment and costs in British Columbia youth, 2003–2013

2021· article· en· W3179194183 on OpenAlexafffundabout
Bonnie J. Leadbeater, Alejandra Contreras, Fahra Rajabali, Alex Zheng, Émilie Beaulieu, Ian Pike

Bibliographic record

VenueInjury Prevention · 2021
Typearticle
Languageen
FieldMedicine
TopicInjury Epidemiology and Prevention
Canadian institutionsUniversity of British ColumbiaSpinal Cord Injury BCUniversity of Victoria
FundersCanadian Institutes of Health Research
KeywordsInjury preventionMedicineOccupational safety and healthPoison controlSuicide preventionCohortYoung adultCohort studyHuman factors and ergonomicsPer capitaDemographyFalling (accident)Environmental healthGerontologyPopulation

Abstract

fetched live from OpenAlex

BACKGROUND: In 2010 in British Columbia (BC), Canada, total injury costs per capita were higher among youth aged 15-24 years than in any other age group. Injury prevention efforts have targeted injuries with high mortality (transportation injuries) or morbidity (concussions). However, the profile and health costs of common youth injuries (types, locations, treatment choices and prevention strategies) and how these change from adolescence to young adulthood is not known. METHODS: Participants (n=662) were a randomly recruited cohort of BC youth, aged 12-18, in 2003. They were followed biennially across a decade (six assessments). RESULTS: Serious injuries (defined as serious enough to limit normal daily activities) in the last year were reported by 27%-41% of participants at each assessment. Most common injuries were sprains or strains, broken bones, cuts, punctures or animal bites, and severe bruises. Most occurred when playing a sport or from falling. Estimated total direct cost of treatment per injury was approximately $2500. In addition, 25% experienced serious injuries at three or more assessments, indicating possible differences that warrents further investigation. CONCLUSIONS: The occurence and health cost of common injuries to youth and young adults are underestimated in this study but are nevertheless substantial. Ongoing surveillence, awareness raising, and prevention efforts may be needed to reduce these costs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.322
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2021
Admission routes3
Has abstractyes

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